Data Architect Jobs at NVIDIA with Visa Sponsorship
NVIDIA's Data Architect roles sit at the intersection of large-scale AI infrastructure and enterprise data strategy. The company has a consistent track record of sponsoring international talent for this function, supporting candidates through the full visa and permanent residence process.
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Get Access To All JobsWould you enjoy researching new algorithms and memory management techniques to accelerate databases on modern computer architectures? Do you like investigating hardware and system bottlenecks, and optimizing performance of data intensive applications? Are you excited about the opportunity to work on the top tier edge of technology with both visibility and impact to the success of a leader like NVIDIA? If so, the Solution Architecture Team invites you to consider this opportunity.
NVIDIA has continuously reinvented itself over two decades. Our invention of the GPU in 1999 sparked the growth of the PC gaming market, redefined modern computer graphics, and revolutionized parallel computing. More recently, GPU deep learning ignited modern AI — the next era of computing. NVIDIA is a “learning machine” that constantly evolves by adapting to new opportunities that are hard to solve, that only we can tackle, and that matter to the world. This is our life’s work, to amplify human imagination and intelligence. Join our team with varied strengths today!
What you will be doing:
- In this role, you will research and develop techniques to GPU-accelerate high performance database, ETL and data analytics applications.
- Work directly with other technical experts in their fields (industry and academia) to perform in-depth analysis and optimization of complex data intensive workloads to ensure the best possible performance of current GPU architectures.
- Influence the design of next-generation hardware architectures, software, and programming models in collaboration with research, hardware, system software, libraries, and tools teams at NVIDIA.
- Influence partners (industry and academia) to push the bounds of data processing with NVIDIA’s full product line.
What we need to see:
- Masters or PhD in Computer Science, Computer Engineering, or related computationally focused science degree or equivalent experience.
- Programming fluency in C/C++ with a deep understanding of algorithms and software design.
- Hands-on experience with low-level parallel programming, e.g. CUDA (preferred), OpenACC, OpenMP, MPI, pthreads, TBB, etc.
- In-depth expertise with CPU/GPU architecture fundamentals, especially memory subsystem.
- Domain expertise in high performance databases, ETL, data analytics and/or vector database.
- Good communication and organization skills, with a logical approach to problem solving, and prioritization skills.
Ways to stand out from the crowd:
- Experience optimizing/implementing database operators or query planner, especially for parallel or distributed frameworks (e.g. production database or Spark).
- Background in optimizing vector database index build and/or search.
- Experience profiling and optimizing CUDA kernels.
- Background with compression, storage systems, networking, and distributed computer architectures.
Data Analytics is one of the rapidly growing fields in GPU accelerated computing. Data preprocessing and data engineering are traditionally CPU based and are becoming the bottleneck for Machine Learning (ML) and Deep Learning (DL) applications, as performance of the frameworks and core ML/DL libraries has been highly optimized leveraging GPUs. Many of today’s applications have complex data analytics pipelines that can benefit from optimizations in memory management, compression, parallel algorithms like sort, search, join, aggregation, groupby, scaling up to multi GPU systems, and scaling out to many nodes. Take a look at some of the open-source projects that NVIDIA employees have worked on: RAPIDS cuDF, NVIDIA nvcomp, NVIDIA Distributed join, NVIDIA cuCollections. NVIDIA is widely considered to be one of the technology world’s most desirable employers. We have some of the most forward-thinking and dedicated people in the world working for us. If you're creative and autonomous, we want to hear from you.
Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 124,000 USD - 195,500 USD for Level 2, and 152,000 USD - 241,500 USD for Level 3.
You will also be eligible for equity and benefits.
Applications for this job will be accepted at least until April 28, 2026.
This posting is for an existing vacancy.
NVIDIA uses AI tools in its recruiting processes.
NVIDIA is committed to fostering a diverse work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.
Would you enjoy researching new algorithms and memory management techniques to accelerate databases on modern computer architectures? Do you like investigating hardware and system bottlenecks, and optimizing performance of data intensive applications? Are you excited about the opportunity to work on the top tier edge of technology with both visibility and impact to the success of a leader like NVIDIA? If so, the Solution Architecture Team invites you to consider this opportunity.
NVIDIA has continuously reinvented itself over two decades. Our invention of the GPU in 1999 sparked the growth of the PC gaming market, redefined modern computer graphics, and revolutionized parallel computing. More recently, GPU deep learning ignited modern AI — the next era of computing. NVIDIA is a “learning machine” that constantly evolves by adapting to new opportunities that are hard to solve, that only we can tackle, and that matter to the world. This is our life’s work, to amplify human imagination and intelligence. Join our team with varied strengths today!
What you will be doing:
- In this role, you will research and develop techniques to GPU-accelerate high performance database, ETL and data analytics applications.
- Work directly with other technical experts in their fields (industry and academia) to perform in-depth analysis and optimization of complex data intensive workloads to ensure the best possible performance of current GPU architectures.
- Influence the design of next-generation hardware architectures, software, and programming models in collaboration with research, hardware, system software, libraries, and tools teams at NVIDIA.
- Influence partners (industry and academia) to push the bounds of data processing with NVIDIA’s full product line.
What we need to see:
- Masters or PhD in Computer Science, Computer Engineering, or related computationally focused science degree or equivalent experience.
- Programming fluency in C/C++ with a deep understanding of algorithms and software design.
- Hands-on experience with low-level parallel programming, e.g. CUDA (preferred), OpenACC, OpenMP, MPI, pthreads, TBB, etc.
- In-depth expertise with CPU/GPU architecture fundamentals, especially memory subsystem.
- Domain expertise in high performance databases, ETL, data analytics and/or vector database.
- Good communication and organization skills, with a logical approach to problem solving, and prioritization skills.
Ways to stand out from the crowd:
- Experience optimizing/implementing database operators or query planner, especially for parallel or distributed frameworks (e.g. production database or Spark).
- Background in optimizing vector database index build and/or search.
- Experience profiling and optimizing CUDA kernels.
- Background with compression, storage systems, networking, and distributed computer architectures.
Data Analytics is one of the rapidly growing fields in GPU accelerated computing. Data preprocessing and data engineering are traditionally CPU based and are becoming the bottleneck for Machine Learning (ML) and Deep Learning (DL) applications, as performance of the frameworks and core ML/DL libraries has been highly optimized leveraging GPUs. Many of today’s applications have complex data analytics pipelines that can benefit from optimizations in memory management, compression, parallel algorithms like sort, search, join, aggregation, groupby, scaling up to multi GPU systems, and scaling out to many nodes. Take a look at some of the open-source projects that NVIDIA employees have worked on: RAPIDS cuDF, NVIDIA nvcomp, NVIDIA Distributed join, NVIDIA cuCollections. NVIDIA is widely considered to be one of the technology world’s most desirable employers. We have some of the most forward-thinking and dedicated people in the world working for us. If you're creative and autonomous, we want to hear from you.
Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 124,000 USD - 195,500 USD for Level 2, and 152,000 USD - 241,500 USD for Level 3.
You will also be eligible for equity and benefits.
Applications for this job will be accepted at least until April 28, 2026.
This posting is for an existing vacancy.
NVIDIA uses AI tools in its recruiting processes.
NVIDIA is committed to fostering a diverse work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.
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Get Access To All JobsTips for Finding Data Architect Jobs at NVIDIA Jobs
Align your portfolio to NVIDIA's data stack
NVIDIA's Data Architect hiring emphasizes experience with distributed data platforms, GPU-accelerated analytics, and cloud-native architectures. Tailor your portfolio to show hands-on work with these systems before you apply, not after an offer arrives.
Target roles flagged for specialty occupation
NVIDIA files Labor Condition Applications with the DOL before H-1B or E-3 petitions can proceed. Roles requiring a directly related degree in computer science, information systems, or data engineering carry the strongest specialty occupation argument, which simplifies your petition.
Clarify your visa category early in screening
NVIDIA sponsors both H-1B and E-3 visas for this function. If you're an Australian citizen, raise E-3 eligibility in your first recruiter conversation. E-3 has no lottery and can be filed any time of year, which removes the timing risk that complicates H-1B offers.
Prepare degree equivalency documentation in advance
USCIS scrutinizes specialty occupation claims closely for Data Architect petitions. If your degree title doesn't map cleanly to the role, gather a credential evaluation and collect employer letters documenting your specialized experience before your start date is set.
Track NVIDIA's open Data Architect roles on Migrate Mate
NVIDIA posts Data Architect positions across multiple teams and business units, and openings move quickly. Use Migrate Mate to filter specifically for NVIDIA roles that offer visa sponsorship so you're applying to confirmed opportunities, not guessing from a general jobs board.
Understand PERM timing if you want a Green Card
NVIDIA supports EB-2 and EB-3 sponsorship for Data Architects, but PERM labor certification runs on a separate timeline from your work visa. Ask your recruiter about the company's process for initiating PERM filings so you can plan your long-term status strategy before your first visa period expires.
Data Architect at NVIDIA jobs are hiring across the US. Find yours.
Find Data Architect at NVIDIA JobsFrequently Asked Questions
Does NVIDIA sponsor H-1B visas for Data Architects?
Yes, NVIDIA sponsors H-1B visas for Data Architect roles. The company has an established process for filing H-1B petitions, which requires your employer to submit a certified Labor Condition Application to the DOL before USCIS can approve the petition. Because H-1B is subject to an annual lottery, your offer timing relative to the April filing window matters for planning your start date.
How do I apply for Data Architect jobs at NVIDIA?
Applications go through NVIDIA's careers site, but finding roles that explicitly include visa sponsorship takes extra filtering. Migrate Mate aggregates NVIDIA's Data Architect openings and surfaces only positions where sponsorship is confirmed, which saves you from applying to roles where international candidates are screened out early. Tailor your resume to reflect experience with GPU-accelerated data systems and distributed architectures before submitting.
Which visa types does NVIDIA commonly use for Data Architects?
NVIDIA sponsors H-1B and E-3 visas for Data Architect positions, along with EB-2 and EB-3 Green Card pathways for longer-term sponsorship. E-3 is available exclusively to Australian citizens and bypasses the H-1B lottery entirely. For candidates already in the U.S. on another status, NVIDIA's immigration team typically coordinates the change of status or transfer process through an in-house or retained immigration counsel.
What qualifications does NVIDIA expect for Data Architect roles?
NVIDIA typically looks for a bachelor's or master's degree in computer science, data engineering, information systems, or a closely related field. Hands-on experience with cloud data platforms, data modeling at scale, and GPU-accelerated analytics pipelines carries significant weight. For H-1B and E-3 petitions, your degree field needs to align directly with the role description, so generic IT backgrounds without specialized data infrastructure experience are harder to support.
How long does the visa sponsorship process take for a Data Architect offer at NVIDIA?
Timeline depends on visa type. E-3 processing at a U.S. consulate abroad typically takes two to four weeks once your employer has a certified LCA from the DOL. H-1B standard processing at USCIS runs three to six months, though premium processing can reduce adjudication to 15 business days. PERM labor certification for Green Card sponsorship runs separately and can take one to two years before an I-140 petition is filed.
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